To meet the increasing demands for high-speed and high-quality data transmission, a multiple-input multiple-output (MIMO) wireless communication system using a plurality of transmit antennas and a plurality of receive antennas is attracting much attention. The MIMO technology performs communications using a plurality of streams via the multiple antennas, to thus greatly enhance a channel capacity, compared to a single antenna. For example, when the transmitting end uses M-ary transmit antennas, the receiving end uses M-ary receive antennas, channels between the antennas are independent of each other, and the bandwidth and the entire transmit power are fixed, the average channel capacity increases by M times the single antenna.
There are various detection schemes for detecting an intended signal from signals received on the receive antennas in the MIMO system. Among the various detection schemes, a maximum likelihood (ML) detection exhibits the highest performance. While a linear scheme such as minimum mean square error (MMSE) detection provides a diversity gain less than the number of the receive antennas, the ML detection guarantees the diversity gain as many as the number of the receive antennas. However, too high operational complexity of the ML detection complicates its applications in spite of the optimum performance.
Recently, researches have been conducted to lower the operational complexity with the performance close to the ML detection. As a result, various approaches such as QR decomposition-modified maximum likelihood detector (QRM-MLD), recursive modified maximum likelihood (RMML), and sorted-RMML (S-RMML) are suggested. Those approaches pertain to the MIMO technology of the open loop (OL). That is, the above-mentioned approaches do not consider how to utilize feedback information of the receiving end and how to generate the feedback information. To apply the ML detection to the MIMO technology of the closed loop (CL), what is needed is a method for generating the feedback information suitable for the ML detection.